RoadSurf: a modelling system for predicting road weather and road surface conditions

Abstract

Forecasting of road surface and traffic conditions is an important aspect of traffic safety and winter road maintenance, especially in the harsh northern climate. The weather conditions can change quickly, for example, with the onset of snowfall or during rapid temperature variations. A prior knowledge of road weather is important from a public road safety standpoint. Proper consideration of upcoming weather events also helps the road maintenance authorities to attend the roads in an effective and economical manner. In Finland, the Finnish Meteorological Institute (FMI) is duty bound to issue warnings of hazardous traffic conditions to the general public. To strengthen these services towards more efficient estimation of rapidly varying conditions of the road surface at a national scale, a simulation model, RoadSurf, has been developed. As input, the model employs numerical weather forecasts, either directly or after modifications made by meteorologists, as well as observations from synoptic or road weather stations and radar precipitation measurement network. As output, the model produces not only road surface temperature, but also road surface condition classification and a traffic index describing the driving conditions in more general terms, as well as road surface friction. The model has been in operational use since 2000. In addition to the original goal of providing road weather forecasts for the national road network, the model has been used in several other applications, for example, in predicting pedestrian sidewalk conditions and in numerous intelligent traffic applications. The present study describes the road weather model RoadSurf and its main applications.

The road weather model RoadSurf driven by the HARMONIE-Climate regional climate model: evaluation over Finland

“…On the other hand, the errors in the precipitation input might have caused the higher RMSE values and lower correlations in April compared to the other months: The biases in the HCLIM-ALARO simulated precipitation were the highest in April. In addition, Kangas et al (2015) December at all stations in 2013 and also in almost every simulated year (not shown) as opposed to the findings by Karsisto et al (2016). In their study, RMSE values of the simulated Troad were larger in October 2013 compared to December 2013.…”

“…This classification is mainly based on the storage terms and Troad. The model physics of RoadSurf are described in more detail in Kangas et al (2015).…”

“…The simulated road weather parameters, such as Troad, have been verified against observations over Finland (Karsisto et al, 2016) and the Netherlands (Karsisto et al, 2017). In addition, Kangas et al (2015) have studied RoadSurf's ability to simulate the amount of water, snow, frost, and ice on the road (called storage terms in RoadSurf) as well as road surface conditions and friction values, although only for two road weather stations in Finland. These studies have considered relatively short verification periods varying from 1 week to some months.…”

“…Road traffic sector is one field benefiting from improved regional climate and weather information, especially at northern high latitudes. These regions do not only experience frequent wintertime snow and ice conditions, but also rapidly changing road weather due to, for instance, the onset of snowfall (Juga et al, 2012) or during temperature variations around the freezing point (Kangas et al, 2015). Systematic consideration of upcoming weather events helps the general public in their every-day commute and, furthermore, road maintenance authorities to attend the roads in a cost-effective manner .…”

“…In Finland, the Finnish Meteorological Institute (FMI) has a duty to issue warnings of hazardous traffic conditions to the general public. To support this, the institute has developed a road weather model RoadSurf which has been in operational use since 2000 (Kangas et al, 2015).…”